Stochastic short-term high-resolution prediction of solar irradiance and photovoltaic power output

Mohammed Olama; Alex Melin; Jin Dong; Seddik Djouadi; Yichen Zhang

doi:10.1109/NAPS.2017.8107309

Stochastic short-term high-resolution prediction of solar irradiance and photovoltaic power output

Mohammed Olama, Alex Melin, Jin Dong, Seddik Djouadi, Yichen Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal's model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our laboratory.

Original language	English
Title of host publication	2017 North American Power Symposium, NAPS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538626993
DOIs	https://doi.org/10.1109/NAPS.2017.8107309
State	Published - Nov 13 2017
Event	2017 North American Power Symposium, NAPS 2017 - Morgantown, United States Duration: Sep 17 2017 → Sep 19 2017

Publication series

Name	2017 North American Power Symposium, NAPS 2017

Conference

Conference	2017 North American Power Symposium, NAPS 2017
Country/Territory	United States
City	Morgantown
Period	09/17/17 → 09/19/17

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Dept. of Energy. ACKNOWLEDGMENT Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy under Contract No. DE-AC05-00OR22725. This manuscript has been authored by a contractor of the U.S. Government under Contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Kalman filter
Photovoltaics
distributed energy resources
expectation-maximization algorithm
solar variability
state-space model
stochastic prediction

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10.1109/NAPS.2017.8107309

Cite this

Olama, M., Melin, A., Dong, J., Djouadi, S., & Zhang, Y. (2017). Stochastic short-term high-resolution prediction of solar irradiance and photovoltaic power output. In 2017 North American Power Symposium, NAPS 2017 Article 8107309 (2017 North American Power Symposium, NAPS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NAPS.2017.8107309

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title = "Stochastic short-term high-resolution prediction of solar irradiance and photovoltaic power output",

abstract = "The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal's model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our laboratory.",

keywords = "Kalman filter, Photovoltaics, distributed energy resources, expectation-maximization algorithm, solar variability, state-space model, stochastic prediction",

author = "Mohammed Olama and Alex Melin and Jin Dong and Seddik Djouadi and Yichen Zhang",

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Olama, M , Melin, A , Dong, J, Djouadi, S & Zhang, Y 2017, Stochastic short-term high-resolution prediction of solar irradiance and photovoltaic power output. in 2017 North American Power Symposium, NAPS 2017., 8107309, 2017 North American Power Symposium, NAPS 2017, Institute of Electrical and Electronics Engineers Inc., 2017 North American Power Symposium, NAPS 2017, Morgantown, United States, 09/17/17. https://doi.org/10.1109/NAPS.2017.8107309

Stochastic short-term high-resolution prediction of solar irradiance and photovoltaic power output. / Olama, Mohammed ; Melin, Alex ; Dong, Jin et al.
2017 North American Power Symposium, NAPS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8107309 (2017 North American Power Symposium, NAPS 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Melin, Alex

AU - Dong, Jin

AU - Djouadi, Seddik

AU - Zhang, Yichen

PY - 2017/11/13

Y1 - 2017/11/13

N2 - The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal's model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our laboratory.

AB - The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal's model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our laboratory.

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KW - distributed energy resources

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BT - 2017 North American Power Symposium, NAPS 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 North American Power Symposium, NAPS 2017

Y2 - 17 September 2017 through 19 September 2017

ER -

Stochastic short-term high-resolution prediction of solar irradiance and photovoltaic power output

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